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Scientific intuition inspired by machine learning-generated hypotheses

Friederich, Pascal ORCID iD icon; Krenn, Mario; Tamblyn, Isaac; Aspuru-Guzik, Alán

Abstract:

Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas. Research focus mostly lies in improving the accuracy of the machine learning models in numerical predictions, while scientific understanding is still almost exclusively generated by human researchers analysing numerical results and drawing conclusions. In this work, we shift the focus on the insights and the knowledge obtained by the machine learning models themselves. In particular, we study how it can be extracted and used to inspire human scientists to increase their intuitions and understanding of natural systems. We apply gradient boosting in decision trees to extract human-interpretable insights from big data sets from chemistry and physics. In chemistry, we not only rediscover widely know rules of thumb but also find new interesting motifs that tell us how to control solubility and energy levels of organic molecules. At the same time, in quantum physics, we gain new understanding on experiments for quantum entanglement. The ability to go beyond numerics and to enter the realm of scientific insight and hypothesis generation opens the door to use machine learning to accelerate the discovery of conceptual understanding in some of the most challenging domains of science.


Verlagsausgabe §
DOI: 10.5445/IR/1000133179
Veröffentlicht am 22.05.2021
Originalveröffentlichung
DOI: 10.1088/2632-2153/abda08
Scopus
Zitationen: 24
Web of Science
Zitationen: 21
Dimensions
Zitationen: 34
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Nanotechnologie (INT)
Institut für Theoretische Informatik (ITI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2632-2153
KITopen-ID: 1000133179
HGF-Programm 43.31.01 (POF IV, LK 01) Multifunctionality Molecular Design & Material Architecture
Erschienen in Machine Learning: Science and Technology
Verlag Institute of Physics Publishing Ltd (IOP Publishing Ltd)
Band 2
Heft 2
Seiten Art.-Nr.: 025027
Nachgewiesen in Web of Science
Dimensions
Scopus
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